Conventionally, clustering techniques perform statistical analysis on data points to create subsets of data points that share common characteristics. The clustering techniques may be utilized by storage devices to organize and store data points. The clustering techniques include hierarchical and partitional clustering. Hierarchical clustering finds successive subsets using previously established subsets, whereas partitional clustering determines all subsets at once. Typically, most clustering techniques are applied to data points and the computation costs associated with the clustering techniques are at least quadratic in the order.
On the other hand, conventional graph partitioning techniques are applied to connected edges and nodes. The graph partitioning techniques do not define subsets that share common characteristics. Rather, graph-partitioning techniques partition graphs into equal partitions while minimizing the number of nodes that are in different partitions.
Generally, conventional clustering techniques and graph partitioning techniques attempt to solve very different problems and operate on data that is formatted differently. Conventional clustering and graph-partitioning techniques fail to provide an integrated solution that partitions large-scale networks and clusters the large-scale networks.